def predict(offer):
from engines import content_engine
num_predictions = 5
offer_list=content_engine.predict(offer, num_predictions)
print("Similar offers recommended for you:")
for s in offer_list:
print(*s)
def predict():
from engines import content_engine
item = request.data.get('item')
num_predictions = request.data.get('num', 10)
if not item:
return []
return content_engine.predict(str(item), num_predictions)
def predict():
from engines import content_engine
item = request.data.get('item')
num_predictions = request.data.get('num', 10)
if not item:
return []
return content_engine.predict(str(item), num_predictions)
def predict():
from engines import content_engine
item = request.get_data('item')
num_predictions = 10
if not item:
return []
return content_engine.predict(str(item), num_predictions,
"id_desc_price.xlsx")
def predict(offer):
from engines import content_engine
num_predictions = 5
print("Similar offers recommended for you:")
similar_offer_list = content_engine.predict(offer, num_predictions)
# prints similar offers along with their similarity score
for s in similar_offer_list:
print(int(s[0]), s[1])
def predict():
from engines import content_engine
item = request.data.get('item')
realID = item
# if item == -1, means prediction for the last row.
if item == '-1':
with open('backup.csv') as source:
reader = csv.DictReader(source.read().splitlines())
realID = str(len(list(reader)) - 1)
num_predictions = request.data.get('num', 10)
data_url = request.data.get('data-url', None)
if not realID:
return []
# For now, only returns a nested list of the top num of post and their scores, need more detailed loggin info!
return content_engine.predict(str(realID), int(num_predictions), data_url)
def predict():
from engines import content_engine
item = request.data.get('item')
realID = item
# if item == -1, means prediction for the last row.
if item == '-1':
with open('backup.csv') as source:
reader = csv.DictReader(source.read().splitlines())
realID = str(len(list(reader)) - 1)
num_predictions = request.data.get('num', 10)
data_url = request.data.get('data-url', None)
if not realID:
return []
# For now, only returns a nested list of the top num of post and their scores, need more detailed loggin info!
return content_engine.predict(str(realID), int(num_predictions), data_url)
